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Statistical Experiment
process that generates outcomes or observations, typically with uncertainty involved
Random Experiment
an experiment whose outcome cannot be predicted
Sample space
set of all possible outcomes
Sample point
an element of sample space
Random
unpredictable or unidentified
Variable
symbol, can take on any of a specified set of values
Random variable
Stochastic variable
rule that assigns a numerical value or characteristic
Two types of random variable
Discrete
Continuous
Discrete random variable
countable, finite can be obtained by counting
Continuous random variable
measurement, infinite, interval
Range space
set of all possible values in a random variable
Discrete Probability Distribution
also known as Probability Mass Function
a table that gives a list of probability values along with their associated value in the range of a discrete random variable
sum of all probability values always equal to 1
Probability Histogram
figure or diagram that contains rectangles centered at each mass point
Expected values
also known as “expectation or payoff value”
is the mean of the probability distribution of the given random variable
Variance
measures the degree of spread of the different values of the random variable about its expected value or mean
tree diagram
a visual tool that makes use of branching connecting lines to represent a certain relationship between the events
horizontal axis
represents the values of the random variable X
vertical axis
gives the corresponding probabilities, P(X)
Mean of a Discrete Random Variable
It is the central value or average of its corresponding probability mass function.
Variance and Standard Deviation of a Discrete Random Variable
describe how scattered or spread out the scores are from the mean value of the random variable.
higher chance of losing
A value of negative indicates that there is a _____
gain
A positive value of indicates a ____ from the deal
normal distribution
is a probability density function for a continuous random variable.
It is also known as the Gaussian distribution in honor of the German Mathematician who derived its equation
Johann Carl Friedrich Gauss
German Mathematician who derived Gaussian distribution
Three Types of Kurtosis
Leptokurtic
Mesokurtic
Platykurtic
Leptokurtic
is a distribution where values are clustered heavily or pile up in the center.
Mesokurtic
is an intermediate distribution which are neither too peaked nor too flat.
Platykurtic
is a flat distribution with values more evenly distributed about the center with broad humps and shot tails.
Inflection points
are the points that mark the change in the curve’s concavity.
Standard Normal Distribution
Is a special normal distribution whose mean is equal to 0 and the standard deviation is 1
z-table
The table used to summarize the approximate areas under the standard normal curve, given in four decimal places.
The values given in the table represents the area under the standard normal curve from 0 (the mean).
Population
refers to the entire group of individuals or objects known to have similar characteristics.
the totality of observations or elements from a set of data.
Sample
a subset of the entire population
refers to one or more elements taken from the population for a specific purpose.
Statistic
a numerical measure or value that describes a sample
Parameter
a numerical measure that describes a characteristic of an entire population.
Probability Sampling
Simple random
Systematic
Stratified
Cluster
Nonprobability Sampling
Convenience
Purposive
Snowball
Quota
Simple Random Sampling
use a sampling frame, a list of individuals in the population
also know as “lottery method”, the most commonly used sampling technique
every element of the population has the same probability of being selected for the inclusion in the sample
Systematic Sampling
adopts a skipping pattern in the selection of sample units
every kth member of the population is selected until the desired number of elements in the sample is obtained
Stratified Sampling
the population is partitioned into subgroups called strata, based on some characteristics like year, religion, gender, age, ethnicity, etc.
samples are then randomly selected separately in each stratum
Cluster Sampling
the total population is divided into clusters that can be pre-existing designation as to cities, towns or provinces.
used when "natural" but relatively heterogeneous groupings are evident in a statistical population.
A random sample of clusters will be taken and all individuals in the selected cluster will be part of the sample
Convenience Sampling
known as grab or opportunity sampling or accidental or haphazard sampling
involves the sample being drawn from that part of the population which is close to hand. That is, readily available and convenient.
Purposive Sampling
done with a purpose in mind
also called judgmental or selective sampling, focuses on samples which are taken based on the judgment of the researcher
Snowball Sampling
sometimes called chain-referral sampling.
the researcher chooses a possible respondent for the study at hand. Then each respondent is asked to give recommendations or referrals to other possible respondents.
used when the research is focused on participants with very specific characteristics such as being members of a gang, victims of domestic violence, etc.
Quota Sampling
equivalent of stratified random sampling in terms of nonprobability sampling.
the researcher starts by identifying quotas, which are predefined control categories such as age, gender, education, or religion.
Sampling Distribution
refers to the probability distributions of statistics, considering that the sample mean may vary from sample to sample
Central Limit Theorem
States that as the sample size n increases, the distribution of the sample means taken with replacement from a population approaches a normal distribution